Integrating data and knowledge to identify functional modules of genes: a multilayer approach

Jiang

et al. 2020

BMC Bioinformatics

Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation. Results: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leaveone-out cross-validation and tenfold cross validation. Conclusions: We demonstrated that network propagation, together with multiomics data, can both discover more partners with similar function, and is unconstricted by the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

Section: Methodsmentioning

confidence: 99%

NPF:network propagation for protein function prediction

Jiang

et al. 2020

BMC Bioinformatics

“…protein-protein interaction, co-regulation, coexpression or membership of a protein complex. Physical PINs directly indicate the cooperation of proteins to drive a biological process [32]. Moreover, computational approaches had successfully detected stable functional modules from co-expression networks [33].…”

Section: Construction Of Multiple Protein Correlation Networkmentioning

confidence: 99%

NPF: Network propagation for protein function prediction

Zhi-hong

Jiang³

et al. 2020

Preprint

Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation.Results: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation.Conclusions: We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the “small-world” feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

“…protein-protein interaction, co-regulation, coexpression or membership of a protein complex. Physical PINs directly indicate the cooperation of proteins to drive a biological process [31]. Moreover, computational approaches had successfully detected stable functional modules from co-expression networks [32].…”

Section: Construction Of Multiple Protein Correlation Networkmentioning

confidence: 99%

NPF：Network propagation for protein function prediction

Jiang

et al. 2020

Preprint

Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, disease treatment and new drug development. Various methods have been developed to facilitate the prediction of functions by combining protein interaction networks (PINs) with multi-omics data. However, how to make full use of multiple biological data to improve the performance of functions annotation is still a dilemma. Results We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. Comprehensive evaluation of NPF indicates that NPF archived higher performance than competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. Conclusions: We demonstrated that network propagation combined with multi-omics data can not only discover more partners with similar function, but also effectively free from the constraints of the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information from protein correlations.